Intelligent Streaming Compliance Framework for Adaptive Financial Crime Detection and Trustworthy Data Governance
Keywords:
Intelligent Streaming Compliance Framework, Financial Crime Detection, Streaming Data Analytics, Regulatory Compliance, Anomaly Detection, Real-Time Risk Monitoring, Data Governance, Event Stream Processing, Trustworthy Analytics, Performance Optimization.Abstract
An Intelligent Streaming Compliance Framework is proposed to facilitate adaptive detection of financial crimes. Anonymised transactional data, system-generated event logs, and alerts raised by monitoring systems are streamed through the functional framework that conforms to data governance policies. The design is modular to support multi-customer and multi-region deployment, allowing customisation for regulatory compliance and local governance framework. The overall architecture is envisioned as components with expected data flow paths, dedicated connectors for integration, and partitioned stewardship responsibility.
Streaming Data Analytics is an efficient way to process data in motion at data velocity with analytics such as anomaly detection, predictive model and trustworthiness process execution not on historical data but operational data. The main motivation for all the methods and techniques in this area is to reduce the execution time of the algorithms while keeping the accuracy of results and quality of data at a level acceptable for the supported applications. Interested function areas experience freshness or near-real-time freshness or seldom not such freshness of results. Latency, Throughput and Accuracy are the three performance measures of a real-time data-processing system.
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